Robust Euclidean alignment of 3D point sets: The trimmed iterative closest point algorithm

Dmitry Chetverikov, Dmitry Stepanov, Pavel Krsek

Research output: Contribution to journalArticle

267 Citations (Scopus)

Abstract

The problem of geometric alignment of two roughly pre-registered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular Iterative Closest Point (ICP) algorithm [IEEE Trans. Pattern Anal. Machine Intell. 14 (1992) 239] is presented, called Trimmed ICP (TrICP). The new algorithm is based on the consistent use of the Least Trimmed Squares approach in all phases of the operation. Convergence is proved and an efficient implementation is discussed. TrICP is fast, applicable to overlaps under 50%, robust to erroneous and incomplete measurements, and has easy-to-set parameters. ICP is a special case of TrICP when the overlap parameter is 100%. Results of a performance evaluation study on the SQUID database of 1100 shapes are presented. The tests compare TrICP and the Iterative Closest Reciprocal Point algorithm [Fifth International Conference on Computer Vision, 1995].

Original languageEnglish
Pages (from-to)299-309
Number of pages11
JournalImage and Vision Computing
Volume23
Issue number3
DOIs
Publication statusPublished - Mar 1 2005

Keywords

  • Iterative closest point
  • Least trimmed squares
  • Point sets
  • Registration
  • Robustness

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

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